Dynamic Pricing Algorithms for Task Allocation in Multi-agent Swarms

  • Prithviraj Dasgupta
  • Matthew Hoeing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5043)


Over the past few years, emergent computing based techniques such as swarming have evolved as an attractive technique to design coordination protocols in large-scale distributed systems and massively multi-agent systems. In this paper, we consider a search-and-execute problem domain where agents have to discover tasks online and perform them in a distributed, collaborative manner. We specifically focus on the problem of distributed coordination between agents to dynamically allocate the tasks among themselves. To address this problem, we describe a novel technique that combines a market-based dynamic pricing algorithm to control the task priorities with a swarming-based coordination technique to disseminate task information across the agents. Experimental results within a simulated environment for a distributed aided target recognition application show that the dynamic pricing based task selection strategies compare favorably with other heuristic-based task selection strategies in terms of task completion times while achieving a significant reduction in communication overhead.


Communication Overhead Dynamic Price Task Allocation Task Selection Task Completion Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Prithviraj Dasgupta
    • 1
  • Matthew Hoeing
    • 1
  1. 1.Computer Science DepartmentUniversity of NebraskaOmaha

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